Perlin noise as a hierarchical neutral landscape model

Thomas R Etherington

doi:10.5194/we-22-1-2022

Abstract

Abstract. Neutral landscape models based on fractal algorithms are used widely by landscape ecologists. Perlin noise is a spectral synthesis algorithm that has not been widely adopted by landscape ecologists, even though the hierarchical properties of Perlin noise are particularly relevant to landscape ecology. To encourage the use of Perlin noise, I provide an explanation of the algorithm in the context of neutral landscape models to demonstrate that the hierarchical nature of the algorithm not only allows for a wide range of possible spatial patterns to be produced, but also usefully parallels theories about the hierarchical structure of landscapes.

Highlights

Neutral landscape models are a key concept and technique in landscape ecology that allow researchers to explore the influence of landscape pattern and structure on ecological processes and methods
I aim to explain Perlin noise in a way that will be intuitive to ecologists, demonstrate how Perlin noise can be used to produce a wide range of useful neutral landscape models, and relate Perlin noise to theories about the hierarchical structure of landscapes
The main advantage of Perlin noise over other fractal methods such as the mid-point displacement method comes from the hierarchical structure of Perlin noise

Summary

Introduction

Neutral landscape models are a key concept and technique in landscape ecology that allow researchers to explore the influence of landscape pattern and structure on ecological processes and methods. A hierarchal fractal neutral landscape model can be created via random curds and implements a process of spatial subdivision where the landscape is repeatedly subdivided by probabilities specific to each hierarchical level to create a landscape in which habitat is more clustered (O’Neill et al, 1992) Another fractal neutral landscape model is based on two-dimensional Brownian motion that uses a parameter that controls the randomness of the motion to produce neutral landscape models with a gradient that can range from highly clustered to highly fragmented (Palmer, 1992). These simple and fractal neutral landscape models are the methods most commonly used by landscape ecologists (Wang and Malanson, 2008). I aim to explain Perlin noise in a way that will be intuitive to ecologists, demonstrate how Perlin noise can be used to produce a wide range of useful neutral landscape models, and relate Perlin noise to theories about the hierarchical structure of landscapes

Perlin noise

Discussion